AI Predicts Brain Shift During Epilepsy Surgery Using Only Pre-Operative Scans

Neurosurgeons face a deceptively simple problem every time they open a skull: the brain refuses to stay where the scan left it. Once the dura mater is incised, cerebrospinal fluid drains away, gravity pulls on the soft tissue, anaesthetic drugs alter blood volume, and surgical manipulation deforms the delicate architecture beneath. The result is brain shift, a deformation that steadily invalidates the preoperative magnetic resonance imaging (MRI) on which neuronavigation systems depend. For a team of researchers and clinicians in London, the answer may lie not in better hardware but in a neural network that learns how brains deform and predicts the distortion before the first incision is made.

In a study published in the International Journal of Computer Assisted Radiology and Surgery, Jingjing Peng of King’s College London and colleagues, working with neurosurgeons at the National Hospital for Neurology and Neurosurgery and King’s College Hospital, introduced NeuralShift, a U-Net-based deep learning framework that predicts a dense brain displacement field using only preoperative MRI and the side of the planned temporal lobe resection. The work targets epilepsy surgery, in which patients with drug-resistant seizures undergo removal of a temporal lobe to eliminate the seizure focus. Because these procedures follow relatively standardised corridors and positioning, they provided an ideal proving ground for a data-driven model of intraoperative deformation.

The clinical stakes are considerable. Neuronavigation aligns preoperative images with the patient’s anatomy in the operating theatre, guiding the surgeon toward the resection target while avoiding critical structures such as the optic radiation, the fibre bundle that carries visual information and sits dangerously close to the surgical corridor. When the brain shifts, the map becomes wrong, and the surgeon may be navigating a phantom. Intraoperative MRI can capture the deformation directly, but it is expensive, time-consuming, disruptive to the surgical workflow, and available in only a minority of operating rooms worldwide. Intraoperative ultrasound offers a faster and cheaper alternative but produces lower-contrast, operator-dependent images that provide limited objective information about deeper structures.

NeuralShift takes a deliberately different route. Rather than attempting to image the brain during surgery, the model learns a cohort-level prior: the statistical pattern of how temporal lobes deform after resection in this patient population. The architecture is built on U-Net, a convolutional network originally designed for biomedical image segmentation, and it produces three outputs simultaneously: a dense three-dimensional displacement field that warps the preoperative scan toward the intraoperative configuration, a prediction of the deformed brain mask, and the corresponding signed distance function, a continuous encoding of the distance to the brain boundary that supplies strong geometric constraints on global shape.

One of the study’s most technically interesting choices lies in how the network was trained. Voxel-wise physical measurements of brain deformation are essentially impossible to obtain, so the researchers used a registration-based surrogate supervision strategy. Each preoperative MRI was registered to its paired post-resection intraoperative MRI using the Fast Free-Form Deformation algorithm implemented in NiftyReg, and the resulting deformation field served as the regression target. The loss function was correspondingly sophisticated, combining a Cartesian mean squared error on displacement vectors with an auxiliary spherical-coordinate loss that penalises errors in the direction and magnitude of each displacement independently, alongside Dice and edge losses for the mask and a mean squared error for the signed distance function. This multi-task objective forces the network to respect both local deformation physics and global brain geometry at the same time.

Careful preprocessing was required to make the paired scans comparable. Intraoperative images contain a surgical cavity and intensity heterogeneity that break standard registration tools, so the team designed a bespoke pipeline. Anatomists manually identified three landmarks on each scan, the anterior commissure, posterior commissure, and intercomissural height, defining a subject-specific coordinate system that standardised orientation before both images were rigidly and affinely registered into the Montreal Neurological Institute template space. Critically, the model was given only a binary hemisphere indicator encoding resection laterality, deliberately excluding any information about the resection cavity itself, because using the cavity segmented from the intraoperative scan would constitute target leakage, revealing an outcome that cannot be known before surgery.

The evaluation was conducted with notable methodological rigour for a feasibility study. From 98 paired preoperative and intraoperative MRI cases, eight were reserved as a fixed validation set for checkpoint selection, and the remaining 90 were divided into nine disjoint test folds of ten cases each. Nine independently initialised models were trained, each evaluated on a fold it had never seen, yielding one out-of-fold prediction for every subject. The results showed consistent improvement: the predicted brain masks achieved an unweighted mean Dice score of 0.97 across folds, up from 0.93 for the undeformed preoperative anatomy, while registration-referenced target registration error at clinically chosen landmarks, including the frontal operculum, the insula, the aqueduct of Sylvius, the corpus callosum, and the optic tract, fell from a range of 1.46 to 4.76 millimetres down to 1.12 to 3.05 millimetres after applying the predicted field.

The authors are unusually candid about the limits of these numbers. Because the landmark reference positions were generated by the same registration algorithm used as the training target, the error reduction measures how faithfully the network reproduces the registration surrogate, not independently verified physical accuracy. The Dice scores describe overlap with masks produced within the study’s own preprocessing workflow rather than manually delineated reference standards. No patient-specific biomechanical model was run on the same cohort, so no claims of equivalence or superiority to physics-based approaches are made. The study is also confined to a single centre with a homogeneous surgical population, meaning the learned deformation prior may not transfer to tumour resections, where oedema, craniotomy geometry, and lesion diversity introduce far greater variability.

Even so, the conceptual contribution is significant. Biomechanical models of brain shift, typically built on finite element methods, require assumptions about tissue elasticity, boundary conditions, loading, and resection geometry, along with a numerical solve that can burden the surgical workflow. NeuralShift, once trained, produces a deformation prior through a single feed-forward pass, without patient-specific meshing or parameter assignment. The trade-off is the absence of explicit physical guarantees and dependence on the deformation patterns represented in the training cohort. The authors position the two paradigms as complementary rather than competing, and they outline a compelling hybrid future in which the learned prior is updated in real time with sparse intraoperative observations such as ultrasound or tracked cortical surface points, preserving rapid inference while adapting to the actual surgical state.

The road ahead includes prospectively recorded resection plans that could localise expected tissue removal more precisely than a hemisphere indicator, explicit separation of true mechanical deformation from the volume change of the resection cavity itself, uncertainty quantification, and multi-centre validation with independently delineated intraoperative surfaces. The code will be released publicly after acceptance. If those steps succeed, the idea that a neural network can anticipate the brain’s silent drift before surgery begins could mark a turning point in image-guided neurosurgery, turning one of the operating room’s oldest frustrations into a solved prediction problem.

Subject of Research: Deep learning prediction of intraoperative brain shift in temporal lobe resection for epilepsy surgery

Article Title: From pre- to intra-operative MRI: predicting brain shift in temporal lobe resection for epilepsy surgery

Article References: Peng, J., Fiore, G., Liu, Y., Ellum, K., Dasgupta, D., Ashkan, K., McEvoy, A., Miserocchi, A., Ourselin, S., Duncan, J., & Granados, A. (2026). From pre- to intra-operative MRI: predicting brain shift in temporal lobe resection for epilepsy surgery. International Journal of Computer Assisted Radiology and Surgery. https://doi.org/10.1007/s11548-026-03794-x

Image Credits: AI Generated

DOI: 10.1007/s11548-026-03794-x

Keywords: brain shift, NeuralShift, U-Net, intraoperative MRI, preoperative MRI, epilepsy surgery, temporal lobe resection, neuronavigation, deep learning, displacement field, target registration error, computer-assisted surgery